Detecting functional decline from normal aging to dementia: Development and validation of a short version of the Amsterdam IADL Questionnaire

Introduction Detecting functional decline from normal aging to dementia is relevant for diagnostic and prognostic purposes. Therefore, the Amsterdam IADL Questionnaire (A-IADL-Q) was developed: a 70-item proxy-based tool with good psychometric properties. We aimed to design a short version while preserving its psychometric quality. Methods Study partners of subjects (n = 1355), ranging from cognitively normal to dementia subjects, completed the original A-IADL-Q. We selected the short version items using a stepwise procedure combining missing data, Item Response Theory, and input from respondents and experts. We investigated internal consistency of the short version and concordance with the original version. To assess its construct validity, we additionally investigated concordance between the short version and the Mini–Mental State Examination (MMSE) and Disability Assessment for Dementia (DAD). Finally, we investigated differences in instrumental activities of daily living (IADL) scores between diagnostic groups across the dementia spectrum. Results We selected 30 items covering the entire spectrum of IADL functioning. Internal consistency (0.98) and concordance with the original version (0.97) were very high. Concordance with the MMSE (0.72) and DAD (0.87) scores was high. IADL impairment scores increased across the spectrum from normal cognition to dementia. Discussion The A-IADL-Q short version (A-IADL-Q-SV) consists of 30 items and has maintained the psychometric quality of the original A-IADL-Q. As such, the A-IADL-Q-SV is a concise measure of functional decline

Blood-based metabolic signatures in Alzheimer's disease

Introduction Identification of blood-based metabolic changes might provide early and easy-to-obtain biomarkers. Methods We included 127 Alzheimer's disease (AD) patients and 121 control subjects with cerebrospinal fluid biomarker-confirmed diagnosis (cutoff tau/amyloid β peptide 42: 0.52). Mass spectrometry platforms determined the concentrations of 53 amine compounds, 22 organic acid compounds, 120 lipid compounds, and 40 oxidative stress compounds. Multiple signatures were assessed: differential expression (nested linear models), classification (logistic regression), and regulatory (network extraction). Results Twenty-six metabolites were differentially expressed. Metabolites improved the classification performance of clinical variables from 74% to 79%. Network models identified five hubs of metabolic dysregulation: tyrosine, glycylglycine, glutamine, lysophosphatic acid C18:2, and platelet-activating factor C16:0. The metabolite network for apolipoprotein E (APOE) ε4 negative AD patients was less cohesive compared with the network for APOE ε4 positive AD patients. Discussion Multiple signatures point to various promising peripheral markers for further validation. The network differences in AD patients according to APOE genotype may reflect different pathways to AD

Circulating metabolites are associated with brain atrophy and white matter hyperintensities

Introduction: Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH). Methods: We studied associations of 143 plasma-based metabolites with MRI measures of brain and hippocampal atrophy and WMH in three independent cohorts (n = 3962). We meta-analyzed the results of linear regression analyses to determine the association of metabolites with MRI measures. Results: Higher glucose levels and lower levels of three small high density lipoprotein (HDL) particles were associated with brain atrophy. Higher glucose levels were associated with WMH. Discussion: Glucose levels were associated with brain atrophy and WMH, and small HDL particle levels were associated with brain atrophy. Circulating metabolites may aid in developing future intervention trials